Rising Temperatures and Drought in Albuquerque, New Mexico
Siddharth Namachivayam
2/22/2019
Background
Above is a map of varying drought conditions across the state of New Mexico in late June during an intense southwest drought which began in 2018. For the vast majority of that year, over 50% of the state was categorized as consistently experiencing ‘severe drought’ by the USDA drought monitor. Unsurprisingly, this drought has had considerable impacts on the state of New Mexico with long-term consequences continuing into 2019. For example, exceedingly dry conditions increased the risk of wildfire and exacerbated water scarcity issues across the state. Farmers became more dependent on pumping groundwater due to reduced water allotments, escalating the cost of crop production. Specifically, in the city of Albuquerque, the drought caused numerous issues with regards to urban water supply, leading to uncertainty around the reliability of reservoirs.
Clearly, extreme events like the recent drought in the southwest have pressing and important implications for the city of Albuquerque and the state of New Mexico. Understanding how such events occur and what factors amplify/facilitate them will be of crucial importance in planning and preparing against the negative impacts of severe drought. Thus, while creating this blog, I was interested in the following question: given the above severity of New Mexico’s ongoing drought, what climatic trends in temperature facilitated and amplified the observed extreme weather behavior?
In order to reduce the scope of my analysis, I decided to strictly look at temperature data from the city of Albuquerque. The analysis of this data is presented below.
Analyzing Albuquerque Data
Above is a plot of Albuquerque maximum temperatures overlayed with a best fit line.
Lets look at some of the results for this regression:
##
## Call:
## lm(formula = TMAX ~ DATES, data = climate_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.689 -8.106 0.825 8.728 20.295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.128e+01 5.608e-02 379.530 <2e-16 ***
## DATES 1.356e-05 5.931e-06 2.287 0.0222 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.85 on 32105 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0001629, Adjusted R-squared: 0.0001317
## F-statistic: 5.23 on 1 and 32105 DF, p-value: 0.02221Note that the p-value for the y-intercept is 2x10^-6 and the p-value for the slope of the line is .02. Both of these values are less than .05. In other words, we can say with 95% confidence that our temperature is increasing at a rate of 1.356x10^-5 degrees C per year and has an intercept of 21.28 degrees C.
Let’s go ahead and clean up our original graph by looking at monthly averages one month at a time.
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9898 -1.4768 -0.0783 1.8627 4.2359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -28.60050 16.91257 -1.691 0.0944 .
## YEAR 0.01882 0.00856 2.198 0.0306 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.04 on 86 degrees of freedom
## Multiple R-squared: 0.05319, Adjusted R-squared: 0.04218
## F-statistic: 4.831 on 1 and 86 DF, p-value: 0.03063
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.3104 -1.4311 0.0408 1.6153 4.7256
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -25.027639 18.641886 -1.343 0.1830
## YEAR 0.018737 0.009438 1.985 0.0503 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.211 on 85 degrees of freedom
## Multiple R-squared: 0.04431, Adjusted R-squared: 0.03307
## F-statistic: 3.941 on 1 and 85 DF, p-value: 0.05034
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0155 -1.3019 -0.1369 1.1707 5.4288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -41.103197 15.697489 -2.618 0.010437 *
## YEAR 0.029099 0.007949 3.661 0.000434 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.894 on 86 degrees of freedom
## Multiple R-squared: 0.1348, Adjusted R-squared: 0.1247
## F-statistic: 13.4 on 1 and 86 DF, p-value: 0.0004336
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1130 -0.8838 -0.2057 1.1431 3.9821
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.495014 14.479181 0.449 0.655
## YEAR 0.007533 0.007332 1.027 0.307
##
## Residual standard error: 1.747 on 86 degrees of freedom
## Multiple R-squared: 0.01212, Adjusted R-squared: 0.0006364
## F-statistic: 1.055 on 1 and 86 DF, p-value: 0.3071
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4346 -1.1179 -0.1707 0.9689 3.9850
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.116514 13.179305 0.919 0.360
## YEAR 0.007294 0.006674 1.093 0.277
##
## Residual standard error: 1.59 on 86 degrees of freedom
## Multiple R-squared: 0.0137, Adjusted R-squared: 0.002231
## F-statistic: 1.195 on 1 and 86 DF, p-value: 0.2775
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1216 -0.8659 -0.0342 0.7996 3.4784
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.720334 11.253071 -0.242 0.80956
## YEAR 0.017713 0.005699 3.108 0.00255 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 86 degrees of freedom
## Multiple R-squared: 0.101, Adjusted R-squared: 0.09054
## F-statistic: 9.661 on 1 and 86 DF, p-value: 0.002551
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3136 -0.8437 -0.1187 0.6718 3.9363
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.812784 10.078899 3.752 0.000318 ***
## YEAR -0.002250 0.005104 -0.441 0.660412
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.216 on 86 degrees of freedom
## Multiple R-squared: 0.002255, Adjusted R-squared: -0.009347
## F-statistic: 0.1944 on 1 and 86 DF, p-value: 0.6604
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1766 -0.7669 -0.0452 0.8890 2.7807
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.528318 10.006676 4.550 1.75e-05 ***
## YEAR -0.006950 0.005068 -1.372 0.174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.208 on 86 degrees of freedom
## Multiple R-squared: 0.0214, Adjusted R-squared: 0.01003
## F-statistic: 1.881 on 1 and 86 DF, p-value: 0.1738
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7412 -1.1391 0.0648 1.0468 3.5288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.616378 11.784895 2.768 0.00691 **
## YEAR -0.002266 0.005968 -0.380 0.70514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.422 on 86 degrees of freedom
## Multiple R-squared: 0.001673, Adjusted R-squared: -0.009935
## F-statistic: 0.1441 on 1 and 86 DF, p-value: 0.7051
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3097 -1.3341 0.0656 1.2140 5.0281
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.293470 14.869752 2.643 0.00978 **
## YEAR -0.008848 0.007530 -1.175 0.24323
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.794 on 86 degrees of freedom
## Multiple R-squared: 0.0158, Adjusted R-squared: 0.004356
## F-statistic: 1.381 on 1 and 86 DF, p-value: 0.2432
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8360 -1.2418 0.1803 1.1735 4.8920
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.320367 15.829233 0.21 0.834
## YEAR 0.005449 0.008016 0.68 0.498
##
## Residual standard error: 1.91 on 86 degrees of freedom
## Multiple R-squared: 0.005345, Adjusted R-squared: -0.006221
## F-statistic: 0.4621 on 1 and 86 DF, p-value: 0.4985
##
## Call:
## lm(formula = TMAX ~ YEAR, data = MonthlyTMAXMean[MonthlyTMAXMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8498 -1.1722 -0.1227 1.3387 4.2202
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.554731 16.036793 1.157 0.250
## YEAR -0.004923 0.008121 -0.606 0.546
##
## Residual standard error: 1.935 on 86 degrees of freedom
## Multiple R-squared: 0.004255, Adjusted R-squared: -0.007323
## F-statistic: 0.3675 on 1 and 86 DF, p-value: 0.546Ok cool. Let’s do the same thing except for TMIN data now instead.
Lets look at some of the results for this regression:
##
## Call:
## lm(formula = TMIN ~ DATES, data = climate_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.630 -7.494 -0.223 8.325 18.334
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.404e+00 5.044e-02 126.97 <2e-16 ***
## DATES 7.037e-05 5.334e-06 13.19 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.859 on 32105 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.005392, Adjusted R-squared: 0.005361
## F-statistic: 174 on 1 and 32105 DF, p-value: < 2.2e-16[SOME ANALYSIS] Let’s go ahead and clean up our original graph by looking at monthly averages one month at a time.
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.121 -1.257 -0.108 1.349 4.224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -71.850088 15.977413 -4.497 2.14e-05 ***
## YEAR 0.034087 0.008087 4.215 6.14e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.927 on 86 degrees of freedom
## Multiple R-squared: 0.1712, Adjusted R-squared: 0.1616
## F-statistic: 17.77 on 1 and 86 DF, p-value: 6.145e-05
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1934 -1.0198 -0.0768 0.9996 5.6681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -66.064772 16.069852 -4.111 9.05e-05 ***
## YEAR 0.032296 0.008136 3.970 0.00015 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.906 on 85 degrees of freedom
## Multiple R-squared: 0.1564, Adjusted R-squared: 0.1465
## F-statistic: 15.76 on 1 and 85 DF, p-value: 0.00015
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3844 -0.8691 0.1963 0.9490 3.0536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -75.898882 11.538049 -6.578 3.55e-09 ***
## YEAR 0.038882 0.005843 6.654 2.52e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 86 degrees of freedom
## Multiple R-squared: 0.3399, Adjusted R-squared: 0.3322
## F-statistic: 44.28 on 1 and 86 DF, p-value: 2.525e-09
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1211 -0.7839 0.0959 0.9470 4.6590
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -43.754036 13.297688 -3.290 0.001452 **
## YEAR 0.024765 0.006734 3.678 0.000409 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.605 on 86 degrees of freedom
## Multiple R-squared: 0.1359, Adjusted R-squared: 0.1258
## F-statistic: 13.52 on 1 and 86 DF, p-value: 0.0004093
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6786 -0.9287 0.1068 1.1252 2.4969
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -33.786160 12.238223 -2.761 0.007049 **
## YEAR 0.022297 0.006198 3.598 0.000535 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.477 on 86 degrees of freedom
## Multiple R-squared: 0.1308, Adjusted R-squared: 0.1207
## F-statistic: 12.94 on 1 and 86 DF, p-value: 0.0005354
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1994 -1.0735 -0.1924 0.8488 3.2773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -51.274069 11.224752 -4.568 1.63e-05 ***
## YEAR 0.033846 0.005684 5.954 5.54e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 86 degrees of freedom
## Multiple R-squared: 0.2919, Adjusted R-squared: 0.2837
## F-statistic: 35.45 on 1 and 86 DF, p-value: 5.541e-08
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.25029 -0.68049 0.01608 0.53894 2.62266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.80398 7.60250 -3.263 0.00158 **
## YEAR 0.02190 0.00385 5.687 1.74e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9174 on 86 degrees of freedom
## Multiple R-squared: 0.2733, Adjusted R-squared: 0.2648
## F-statistic: 32.34 on 1 and 86 DF, p-value: 1.743e-07
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.09133 -0.63185 0.03808 0.53974 2.11021
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17.158922 7.409235 -2.316 0.0229 *
## YEAR 0.017570 0.003752 4.683 1.05e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8941 on 86 degrees of freedom
## Multiple R-squared: 0.2032, Adjusted R-squared: 0.1939
## F-statistic: 21.93 on 1 and 86 DF, p-value: 1.049e-05
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3999 -0.8945 0.1578 0.9943 2.4423
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -26.352264 10.398567 -2.534 0.013081 *
## YEAR 0.020289 0.005266 3.853 0.000224 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.255 on 86 degrees of freedom
## Multiple R-squared: 0.1472, Adjusted R-squared: 0.1373
## F-statistic: 14.84 on 1 and 86 DF, p-value: 0.0002244
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4268 -0.8014 0.0885 1.0423 4.0343
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -35.222087 11.800345 -2.985 0.003694 **
## YEAR 0.021392 0.005976 3.580 0.000569 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.424 on 86 degrees of freedom
## Multiple R-squared: 0.1297, Adjusted R-squared: 0.1196
## F-statistic: 12.81 on 1 and 86 DF, p-value: 0.0005686
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1755 -0.6861 0.0688 0.7318 2.9373
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -73.634316 12.187575 -6.042 3.79e-08 ***
## YEAR 0.037306 0.006172 6.044 3.74e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.471 on 86 degrees of freedom
## Multiple R-squared: 0.2982, Adjusted R-squared: 0.29
## F-statistic: 36.54 on 1 and 86 DF, p-value: 3.744e-08
##
## Call:
## lm(formula = TMIN ~ YEAR, data = MonthlyTMINMean[MonthlyTMINMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8536 -1.0239 0.1069 0.8080 3.3587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -40.380523 12.413131 -3.253 0.00163 **
## YEAR 0.018462 0.006286 2.937 0.00425 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.498 on 86 degrees of freedom
## Multiple R-squared: 0.09116, Adjusted R-squared: 0.08059
## F-statistic: 8.626 on 1 and 86 DF, p-value: 0.004252Ok lets take a break from all this temprature stuff and look at precipitation instead.
##
## Call:
## lm(formula = PRCP ~ DATES, data = climate_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.628 -0.614 -0.600 -0.586 48.205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.021e-01 1.420e-02 42.410 <2e-16 ***
## DATES 1.451e-06 1.502e-06 0.966 0.334
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.494 on 32105 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 2.907e-05, Adjusted R-squared: -2.077e-06
## F-statistic: 0.9333 on 1 and 32105 DF, p-value: 0.334[SOME ANALYSIS] Let’s go ahead and clean up our original graph by looking at monthly averages one month at a time.
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.32870 -0.22164 -0.07728 0.15257 0.80618
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2588363 2.3338247 -0.111 0.912
## YEAR 0.0002917 0.0011813 0.247 0.806
##
## Residual standard error: 0.2815 on 86 degrees of freedom
## Multiple R-squared: 0.0007087, Adjusted R-squared: -0.01091
## F-statistic: 0.06099 on 1 and 86 DF, p-value: 0.8055
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.41037 -0.21722 -0.07784 0.13403 1.26236
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.444710 2.795373 -0.875 0.384
## YEAR 0.001421 0.001415 1.004 0.318
##
## Residual standard error: 0.3315 on 85 degrees of freedom
## Multiple R-squared: 0.01172, Adjusted R-squared: 9.686e-05
## F-statistic: 1.008 on 1 and 85 DF, p-value: 0.3182
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.40001 -0.27495 -0.05395 0.12151 1.52118
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1674930 3.1514270 -0.053 0.958
## YEAR 0.0002815 0.0015959 0.176 0.860
##
## Residual standard error: 0.3803 on 86 degrees of freedom
## Multiple R-squared: 0.0003616, Adjusted R-squared: -0.01126
## F-statistic: 0.03111 on 1 and 86 DF, p-value: 0.8604
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4430 -0.3746 -0.1303 0.1628 2.1182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.140537 4.114377 0.277 0.782
## YEAR -0.000357 0.002084 -0.171 0.864
##
## Residual standard error: 0.4965 on 86 degrees of freedom
## Multiple R-squared: 0.0003412, Adjusted R-squared: -0.01128
## F-statistic: 0.02935 on 1 and 86 DF, p-value: 0.8644
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5895 -0.3430 -0.1631 0.1220 1.9195
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.174908 4.381272 1.866 0.0655 .
## YEAR -0.003906 0.002219 -1.760 0.0819 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5287 on 86 degrees of freedom
## Multiple R-squared: 0.03478, Adjusted R-squared: 0.02356
## F-statistic: 3.099 on 1 and 86 DF, p-value: 0.08189
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6167 -0.3914 -0.1864 0.2100 2.5908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.730782 4.721300 1.214 0.228
## YEAR -0.002637 0.002391 -1.103 0.273
##
## Residual standard error: 0.5697 on 86 degrees of freedom
## Multiple R-squared: 0.01395, Adjusted R-squared: 0.002487
## F-statistic: 1.217 on 1 and 86 DF, p-value: 0.2731
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.12039 -0.54223 -0.09456 0.46470 1.60949
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.596604 5.663067 -1.695 0.0938 .
## YEAR 0.005447 0.002868 1.899 0.0609 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6834 on 86 degrees of freedom
## Multiple R-squared: 0.04026, Adjusted R-squared: 0.0291
## F-statistic: 3.607 on 1 and 86 DF, p-value: 0.06088
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2007 -0.5919 -0.1190 0.5910 1.8927
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.4929329 6.3526572 0.392 0.696
## YEAR -0.0006586 0.0032171 -0.205 0.838
##
## Residual standard error: 0.7666 on 86 degrees of freedom
## Multiple R-squared: 0.0004871, Adjusted R-squared: -0.01114
## F-statistic: 0.04191 on 1 and 86 DF, p-value: 0.8383
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8806 -0.4796 -0.1026 0.2953 2.4001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.773380 5.067820 -0.547 0.586
## YEAR 0.001855 0.002566 0.723 0.472
##
## Residual standard error: 0.6116 on 86 degrees of freedom
## Multiple R-squared: 0.006035, Adjusted R-squared: -0.005522
## F-statistic: 0.5222 on 1 and 86 DF, p-value: 0.4719
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8059 -0.4904 -0.2181 0.3535 1.8356
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.015153 5.398149 -0.929 0.355
## YEAR 0.002893 0.002734 1.058 0.293
##
## Residual standard error: 0.6514 on 86 degrees of freedom
## Multiple R-squared: 0.01286, Adjusted R-squared: 0.001378
## F-statistic: 1.12 on 1 and 86 DF, p-value: 0.2929
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.41004 -0.31896 -0.07345 0.20705 1.23717
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.6268229 3.1019411 -0.202 0.840
## YEAR 0.0005138 0.0015709 0.327 0.744
##
## Residual standard error: 0.3743 on 86 degrees of freedom
## Multiple R-squared: 0.001242, Adjusted R-squared: -0.01037
## F-statistic: 0.107 on 1 and 86 DF, p-value: 0.7444
##
## Call:
## lm(formula = PRCP ~ YEAR, data = MonthlyPRCPMean[MonthlyPRCPMean$MONTH ==
## i, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.47023 -0.28094 -0.08435 0.22301 1.12223
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.184282 2.996524 -0.729 0.468
## YEAR 0.001316 0.001517 0.867 0.388
##
## Residual standard error: 0.3616 on 86 degrees of freedom
## Multiple R-squared: 0.00867, Adjusted R-squared: -0.002857
## F-statistic: 0.7522 on 1 and 86 DF, p-value: 0.3882Sources to look at: https://onlinelibrary.wiley.com/doi/full/10.1002/eco.1849 http://adsabs.harvard.edu/abs/2016AGUFMGC32C..04S https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.12743 https://www.sciencedirect.com/science/article/pii/S0190052816300323 https://bioone.org/journals/Rangelands/volume-30/issue-3/1551-501X(2008)30[23:CCAEOT]2.0.CO;2/Climate-Change-and-Ecosystems-of-the-Southwestern-United-States/10.2111/1551-501X(2008)30[23:CCAEOT]2.0.CO;2.full https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008GL035075